Drinking water is an important source of exposure to environmental contaminants. Many organic compounds commonly found in drinking water (e.g., benzene, chloroform) are believed to be carcinogenic. Understanding current national exposures and the costs of reducing these exposures will allow us to prioritize among different health risks and focus efforts on those areas likely to have the greatest public health benefits. The specific objectives of this project are to develop joint statistical models of the concentrations of organic contaminants in U.S. drinking water, to estimate human exposure to these contaminants from drinking water, and to estimate the cost-effectiveness of efforts to reduce these exposures. Multivariate statistical models of organic contaminant concentrations in U.S. drinking water will be estimated using data in one or more publicly available databases on drinking water quality. A cross-validation approach will be used to identify the multivariate statistical model with the greatest predictive power. Predictions from the selected model will be used in conjunction with information on the distribution of the population served by public water supplies in the U.S. to develop a national distribution of human exposure to these contaminants. The cost-effectiveness of risk mitigation options will be estimated by a statistical simulation of the effects of potential future drinking water standards. Finished water concentrations at each of the nation's approximately 55,000 community water systems will be simulated from statistical concentrations exceeding their potential future standards, the costs and exposure reductions achieved by additional treatment processes will be simulated. Incorporating joint models of contaminant occurrence in such benefit-cost models is important because treatments implemented to reduce exposure to one contaminant often affect the concentrations of other contaminants. This project will develop more accurate models of current human exposures to organic contaminants in drinking water and will also improve estimates and benefits of reducing human exposure to these contaminants.